Serialization

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Chapter2.tex

@@ -1,5 +1,6 @@
 \TUchapter{RELATED WORKS}
-
+Many authors and researchers have developed or extended attack graphs since their beginning
+as attack trees. This Chapter reviews a few of their efforts as they relate to this work.
 \TUsection{Introduction to Graph Generation}
 
 \TUsection{Improvements to Attack Graph Generation} \label{sec:related_works}

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Chapter5.tex

@@ -1,13 +1,56 @@
-\TUchapter{IMPLEMENTATION OF MESSAGE PASSING INTERFACE}
+\TUchapter{Utilization OF MESSAGE PASSING INTERFACE}
 
 \TUsection{Introduction to MPI Utilization for Attack Graph Generation}
 
 \TUsection{Necessary Components}
 \TUsubsection{Serialization}
+In order to distribute workloads across nodes in a distributed system, various
+types of data will need to be sent and received. Support and mechanisms vary based 
+on the MPI implementation, but most fundamental data types such as integers, doubles,
+characters, and Booleans are incorporated into the MPI implementation. While this does
+simplify some of the messages that need to be sent and received in the MPI approaches of
+attack graph generation, it does not cover the vast majority of them.
+
+RAGE implements many custom classes and structs that are used throughout the generation process.
+Qualities, topologies, network states, and exploits are a few such examples. Rather than breaking
+each of these down into fundamental types manually, serialization functions are leveraged to handle
+most of this. RAGE already incorporates Boost graph libraries for auxiliary support, so this work
+extended this further to utilize the serialization libraries also provided by Boost. These
+libraries also include support for serializing all STL classes, and many of the RAGE 
+classes have members that make use of the STL classes. One additional advantage of the Boost 
+library approach is that many of the RAGE class members are nested. For example, the NetworkState
+class has a member vector of Quality classes. When serializing the NetworkState class, boost will
+recursively serialize all members, including the custom class members, assuming they also have
+serialization functions.
+
+When using the serialization libraries, this work opted to use the intrusive route, where the
+class instances are altered directly. This was preferable to the non-intrusive approach, since 
+the class instances were able to be altered with relative ease, and many of the class instances 
+did not expose enough information for the non-intrusive approach to be viable.  
 \TUsubsection{Data Consistency}
 
 \TUsection{Tasking Approach}
 \TUsubsection{Introduction to the Tasking Approach}
+The high-level overview of the RAGE Data Flow Diagram was presented by the author of
+\cite{cook_rage_2018}, and can be seen in Figure \ref{fig:RAGE_chart}. This diagram 
+includes an attack graph generation block that can be broken down into six main tasks.
+These tasks are described in Figure \ref{fig:tasks}. Prior works such as that seen by the 
+authors of \cite{li_concurrency_2019} work to parallelize the attack graph generation using
+OpenMP by dividing the frontier. This approach, however, utilizes Message Passing Interface (MPI)
+to distribute the six tasks to examine the effect on speedup, efficiency, and scalability for 
+attack graph generation.
+\begin{figure}[htp]
+    \includegraphics[width=\linewidth]{"./Chapter5_img/RAGE_Chart.png"}
+    \vspace{.2truein} \centerline{}
+        \caption{Generation Flowchart of RAGE}
+        \label{fig:RAGE_chart}
+\end{figure}
+\begin{figure}[htp]
+    \includegraphics[width=\linewidth]{"./Chapter5_img/horiz_task.drawio.png"}
+    \vspace{.2truein} \centerline{}
+        \caption{Task Overview of the Attack Graph Generation Process}
+        \label{fig:tasks}
+\end{figure}
 
 \TUsubsection{Algorithm Design}
 \TUsubsubsection{Communication Structure}

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Schrick-Noah_MS-Thesis.aux

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Schrick-Noah_MS-Thesis.toc

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-\contentsline {subsection}{\numberline {6.1.3}\it Analysis}{15}{}%
+\contentsline {subsection}{\numberline {5.4.1}\it Introduction to the Subgraphing Approach}{17}{}%
-\contentsline {section}{\numberline {6.2}\bf Large Networks}{15}{}%
+\contentsline {subsection}{\numberline {5.4.2}\it Algorithm Design}{17}{}%
-\contentsline {subsection}{\numberline {6.2.1}\it Test Information}{15}{}%
+\contentsline {subsubsection}{Communication Structure}{17}{}%
-\contentsline {subsection}{\numberline {6.2.2}\it Results}{15}{}%
+\contentsline {subsubsection}{Worker Nodes}{17}{}%
-\contentsline {subsection}{\numberline {6.2.3}\it Analysis}{15}{}%
+\contentsline {subsubsection}{Root Node}{17}{}%
-\contentsline {section}{\numberline {6.3}\bf Large Exploit Lists}{15}{}%
+\contentsline {subsubsection}{Database Node}{17}{}%
-\contentsline {subsection}{\numberline {6.3.1}\it Test Information}{15}{}%
+\contentsline {subsection}{\numberline {5.4.3}\it Performance Expectations}{17}{}%
-\contentsline {subsection}{\numberline {6.3.2}\it Results}{15}{}%
+\contentsline {chapter}{\numberline {CHAPTER 6: }{\bf \uppercase {PERFORMANCE ANALYSIS}}}{18}{}%
-\contentsline {subsection}{\numberline {6.3.3}\it Analysis}{15}{}%
+\contentsline {section}{\numberline {6.1}\bf Small Networks}{18}{}%
-\contentsline {section}{\numberline {6.4}\bf Distributed Hash Tables}{15}{}%
+\contentsline {subsection}{\numberline {6.1.1}\it Test Information}{18}{}%
-\contentsline {subsection}{\numberline {6.4.1}\it Test Information}{16}{}%
+\contentsline {subsection}{\numberline {6.1.2}\it Results}{18}{}%
-\contentsline {subsection}{\numberline {6.4.2}\it Results}{16}{}%
+\contentsline {subsection}{\numberline {6.1.3}\it Analysis}{18}{}%
-\contentsline {subsection}{\numberline {6.4.3}\it Analysis}{16}{}%
+\contentsline {section}{\numberline {6.2}\bf Large Networks}{18}{}%
-\contentsline {chapter}{\numberline {CHAPTER 7: }{\bf \uppercase {CONCLUSIONS AND FUTURE WORKS}}}{17}{}%
+\contentsline {subsection}{\numberline {6.2.1}\it Test Information}{18}{}%
-\contentsline {section}{\numberline {7.1}\bf Future Work}{17}{}%
+\contentsline {subsection}{\numberline {6.2.2}\it Results}{18}{}%
+\contentsline {subsection}{\numberline {6.2.3}\it Analysis}{18}{}%
+\contentsline {section}{\numberline {6.3}\bf Large Exploit Lists}{18}{}%
+\contentsline {subsection}{\numberline {6.3.1}\it Test Information}{18}{}%
+\contentsline {subsection}{\numberline {6.3.2}\it Results}{18}{}%
+\contentsline {subsection}{\numberline {6.3.3}\it Analysis}{18}{}%
+\contentsline {section}{\numberline {6.4}\bf Distributed Hash Tables}{18}{}%
+\contentsline {subsection}{\numberline {6.4.1}\it Test Information}{19}{}%
+\contentsline {subsection}{\numberline {6.4.2}\it Results}{19}{}%
+\contentsline {subsection}{\numberline {6.4.3}\it Analysis}{19}{}%
+\contentsline {chapter}{\numberline {CHAPTER 7: }{\bf \uppercase {CONCLUSIONS AND FUTURE WORKS}}}{20}{}%
+\contentsline {section}{\numberline {7.1}\bf Future Work}{20}{}%
 {\hfill \ }
-\contentsline {section}{\hspace {-\parindent }NOMENCLATURE}{18}{}%
+\contentsline {section}{\hspace {-\parindent }NOMENCLATURE}{21}{}%
 \addvspace {10pt}
-\contentsline {section}{\hspace {-\parindent }BIBLIOGRAPHY}{18}{}%
+\contentsline {section}{\hspace {-\parindent }BIBLIOGRAPHY}{21}{}%
 {\hfill \ }
-\contentsline {section}{\hspace {-\parindent }APPENDIX A:\ \ {\bf \uppercase {THE FIRST APPENDIX}} }{20}{}%
+\contentsline {section}{\hspace {-\parindent }APPENDIX A:\ \ {\bf \uppercase {THE FIRST APPENDIX}} }{23}{}%
-\contentsline {section}{\hspace {-\parindent }APPENDIX B:\ \ {\bf \uppercase {THE SECOND APPENDIX}} }{21}{}%
+\contentsline {section}{\hspace {-\parindent }APPENDIX B:\ \ {\bf \uppercase {THE SECOND APPENDIX}} }{24}{}%
-\contentsline {section}{\numberline {B.1}\bf A Heading in an Appendix}{21}{}%
+\contentsline {section}{\numberline {B.1}\bf A Heading in an Appendix}{24}{}%
-\contentsline {subsection}{\numberline {B.1.1}\it A Subheading in an Appendix}{21}{}%
+\contentsline {subsection}{\numberline {B.1.1}\it A Subheading in an Appendix}{24}{}%
-\contentsline {subsubsection}{A Sub-subsection in an Appendix}{21}{}%
+\contentsline {subsubsection}{A Sub-subsection in an Appendix}{24}{}%